---
title: "llm-course vs tree-of-thought-llm"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-princeton-nlp-tree-of-thought-llm"
tools: ["mlabonne-llm-course", "princeton-nlp-tree-of-thought-llm"]
---

# llm-course vs tree-of-thought-llm

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks; pick tree-of-thought-llm if the 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure.

[llm-course](https://mlabonne.github.io/blog/) reports 83k GitHub stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. [tree-of-thought-llm](https://arxiv.org/abs/2305.10601) has 6.0k stars, 624 forks, and 8 open issues, last pushed Jan 16, 2025. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [tree-of-thought-llm's repository](https://github.com/princeton-nlp/tree-of-thought-llm).

| | [llm-course](/tools/mlabonne-llm-course.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models |
| Stars | 83,011 | 6,048 |
| Forks | 9,657 | 624 |
| Open issues | 90 | 8 |
| Language | - | Python |
| Adopt for | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | The 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [llm-course](/tools/mlabonne-llm-course.md) | [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 224d | 577d |
| Open issues (now) | 90 | 8 |
| Stars delta | +1.5k (30d) | +18 (30d) |
| Open issues delta | +4 (30d) | 0 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/princeton-nlp-tree-of-thought-llm/trust.md) |

## Shared compatibility

- **Python**: [llm-course](/tools/mlabonne-llm-course.md) - Python runtime; [tree-of-thought-llm](/tools/princeton-nlp-tree-of-thought-llm.md) - Python runtime

## Decision facts: llm-course

- **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks.

## Decision facts: tree-of-thought-llm

- **Requirements:** Min 4 GB RAM
- **Adopt for:** The 'Tree of Thoughts' approach provides a structured way to deliberate problem-solving using large language models and is well-suited for tasks requiring exploration through a tree-like structure.

## Choose when

### Choose llm-course if…

- License: llm-course is Apache-2.0, tree-of-thought-llm is MIT.
- Tags unique to llm-course: course, machine-learning, roadmap.
- Also covers Developer Tools, Evaluation & Observability, Inference & Serving.
- Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.

### Choose tree-of-thought-llm if…

- License: tree-of-thought-llm is MIT, llm-course is Apache-2.0.
- Requirements: Min 4 GB RAM.
- Tags unique to tree-of-thought-llm: prompting, tree-of-thoughts, tree-search.
- - Use 'tree-of-thought-llm' when you need an approach that handles deliberative reasoning problems, like the game of 24, leveraging large language models.

## When NOT to use llm-course

- Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation.
- Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum.
- Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.

## When NOT to use tree-of-thought-llm

- - Avoid using 'tree-of-thought-llm' for problems that do not benefit from tree-like exploration or where the solution does not involve deliberate reasoning or step-by-step evaluation.
- - If real-time decision-making is critical and computational resources are limited, this tool might be too slow due to its reliance on large language models like GPT-4 which may introduce latency.

## Common questions

### What is the difference between llm-course and tree-of-thought-llm?

llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. tree-of-thought-llm: [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models. See the comparison table for live GitHub stats and shared categories.

### When should I choose llm-course over tree-of-thought-llm?

Choose llm-course over tree-of-thought-llm when License: llm-course is Apache-2.0, tree-of-thought-llm is MIT; Tags unique to llm-course: course, machine-learning, roadmap; Also covers Developer Tools, Evaluation & Observability, Inference & Serving; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment.

### When should I choose tree-of-thought-llm over llm-course?

Choose tree-of-thought-llm over llm-course when License: tree-of-thought-llm is MIT, llm-course is Apache-2.0; Requirements: Min 4 GB RAM; Tags unique to tree-of-thought-llm: prompting, tree-of-thoughts, tree-search; - Use 'tree-of-thought-llm' when you need an approach that handles deliberative reasoning problems, like the game of 24, leveraging large language models.

### When should I avoid llm-course?

Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path.

### When should I avoid tree-of-thought-llm?

- Avoid using 'tree-of-thought-llm' for problems that do not benefit from tree-like exploration or where the solution does not involve deliberate reasoning or step-by-step evaluation. - If real-time decision-making is critical and computational resources are limited, this tool might be too slow due to its reliance on large language models like GPT-4 which may introduce latency.

### Is llm-course or tree-of-thought-llm more popular on GitHub?

llm-course has more GitHub stars (83,011 vs 6,048). Stars measure visibility, not whether either tool fits your constraints.

### Are llm-course and tree-of-thought-llm open source?

Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, tree-of-thought-llm: MIT).

### Where can I find alternatives to llm-course or tree-of-thought-llm?

GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [tree-of-thought-llm alternatives](/tools/princeton-nlp-tree-of-thought-llm/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [tree-of-thought-llm markdown twin](/tools/princeton-nlp-tree-of-thought-llm/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/mlabonne-llm-course-vs-princeton-nlp-tree-of-thought-llm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, llm-course or tree-of-thought-llm?

llm-course: Slowing. tree-of-thought-llm: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for llm-course and tree-of-thought-llm?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [tree-of-thought-llm trust report](/tools/princeton-nlp-tree-of-thought-llm/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_